facehires improvements and fixes

modules/modelloader.py

@@ -377,6 +377,8 @@ def download_url_to_file(url: str, dst: str):
 
 def load_file_from_url(url: str, *, model_dir: str, progress: bool = True, file_name = None): # pylint: disable=unused-argument
     """Download a file from url into model_dir, using the file present if possible. Returns the path to the downloaded file."""
+    if model_dir is None:
+        shared.log.error('Download folder is none')
     os.makedirs(model_dir, exist_ok=True)
     if not file_name:
         parts = urlparse(url)
@@ -398,14 +400,15 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
     @param ext_filter: An optional list of filename extensions to filter by
     @return: A list of paths containing the desired model(s)
     """
-    places = list(set([model_path, command_path])) # noqa:C405
+    places = [x for x in list(set([model_path, command_path])) if x is not None] # noqa:C405
     output = []
     try:
         output:list = [*files_cache.list_files(*places, ext_filter=ext_filter, ext_blacklist=ext_blacklist)]
         if model_url is not None and len(output) == 0:
             if download_name is not None:
                 dl = load_file_from_url(model_url, model_dir=places[0], progress=True, file_name=download_name)
-                output.append(dl)
+                if dl is not None:
+                    output.append(dl)
             else:
                 output.append(model_url)
     except Exception as e:

modules/processing_class.py

@@ -242,7 +242,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if all_subseeds is not None:
             self.all_subseeds = all_subseeds
 
-    def init_hr(self, scale = None, upscaler = None):
+    def init_hr(self, scale = None, upscaler = None, force = False): # pylint: disable=unused-argument
         scale = scale or self.hr_scale
         upscaler = upscaler or self.hr_upscaler
         if self.hr_resize_x == 0 and self.hr_resize_y == 0:

scripts/face-details.py

@@ -92,7 +92,7 @@ class FaceRestorerYolo(FaceRestoration):
         from modules import devices, processing_class
         if not hasattr(p, 'facehires'):
             p.facehires = 0
-        if np_image is None or getattr(p, 'facehires', 0) >= p.batch_size:
+        if np_image is None or p.facehires >= p.batch_size:
             return np_image
         self.load()
         if self.model is None:
@@ -109,9 +109,33 @@ class FaceRestorerYolo(FaceRestoration):
         orig_cls = p.__class__
 
         pp = None
-        p.facehires += 1 # set flag to avoid recursion
         shared.opts.data['mask_apply_overlay'] = True
-        p = processing_class.switch_class(p, processing.StableDiffusionProcessingImg2Img)
+        args = {
+            'batch_size': 1,
+            'n_iter': 1,
+            'inpaint_full_res': True,
+            'inpainting_mask_invert': 0,
+            'inpainting_fill': 1, # no fill
+            'sampler_name': orig_p.get('hr_sampler_name', 'default'),
+            'steps': orig_p.get('hr_second_pass_steps', 0),
+            'negative_prompt': orig_p.get('refiner_negative', ''),
+            'denoising_strength': orig_p.get('denoising_strength', 0.3),
+            'styles': [],
+            'prompt': orig_p.get('refiner_prompt', ''),
+            # TODO facehires expose as tunable
+            'mask_blur': 10,
+            'inpaint_full_res_padding': 15,
+            'restore_faces': True,
+        }
+        p = processing_class.switch_class(p, processing.StableDiffusionProcessingImg2Img, args)
+        p.facehires += 1 # set flag to avoid recursion
+
+        if p.steps < 1:
+            p.steps = orig_p.get('steps', 0)
+        if len(p.prompt) == 0:
+            p.prompt = orig_p.get('all_prompts', [''])[0]
+        if len(p.negative_prompt) == 0:
+            p.negative_prompt = orig_p.get('all_negative_prompts', [''])[0]
 
         for face in faces:
             if face.mask is None:
@@ -121,29 +145,15 @@ class FaceRestorerYolo(FaceRestoration):
                 continue
             p.init_images = [image]
             p.image_mask = [face.mask]
-            p.inpaint_full_res = True
-            p.inpainting_mask_invert = 0
-            p.inpainting_fill = 1 # no fill
-            p.sampler_name = orig_p.get('hr_sampler_name', 'default')
-            p.steps = orig_p.get('hr_second_pass_steps', p.steps)
-            p.denoising_strength = orig_p.get('denoising_strength', 0.3)
-            p.styles = []
-            p.prompt = orig_p.get('refiner_prompt', '')
-            if len(p.prompt) == 0:
-                p.prompt = orig_p.get('all_prompts', [''])[0]
-            p.negative_prompt = orig_p.get('refiner_negative', '')
-            if len(p.negative_prompt) == 0:
-                p.negative_prompt = orig_p.get('all_negative_prompts', [''])[0]
-            # TODO facehires expose as tunable
-            p.mask_blur = 10
-            p.inpaint_full_res_padding = 15
-            p.restore_faces = True
-            shared.log.debug(f'Face HiRes: {face.__dict__} strength={p.denoising_strength} blur={p.mask_blur} padding={p.inpaint_full_res_padding}')
+            shared.log.debug(f'Face HiRes: face={p.facehires} {face.__dict__} strength={p.denoising_strength} blur={p.mask_blur} padding={p.inpaint_full_res_padding} steps={p.steps}')
             pp = processing.process_images_inner(p)
             p.overlay_images = None # skip applying overlay twice
             if pp is not None and pp.images is not None and len(pp.images) > 0:
                 image = pp.images[0]
 
+        if np_image is None or getattr(p, 'facehires', 0) >= p.batch_size:
+            p.facehires = 0
+
         # restore pipeline
         p = processing_class.switch_class(p, orig_cls, orig_p)
         shared.opts.data['mask_apply_overlay'] = orig_apply_overlay